Image-Based Time Series Representations for Pixelwise Eucalyptus Region Classification: A Comparative Study

Autor(es)

Dias, Danielle ^[1] ; Dias, Ulisses ^[2] ; Menini, Nathalia ^[1] ; Lamparelli, Rubens ^[3] ; Le Maire, Guerric ^[4] ; Torres, Ricardo da S. ^[5]

Afiliação do(s) autor(es)

^[1] Univ Campinas UNICAMP, Inst Comp, BR-13083970 Campinas - Brazil

^[2] Univ Campinas UNICAMP, Sch Technol, BR-13484350 Limeira - Brazil

^[3] Univ Campinas UNICAMP, Nucleo Interdisciplinar Planejamento Energet, BR-13083970 Campinas - Brazil

^[4] Univ Montpellier, Eco&Sols, CIRAD, INRA, IRD, Montpellier SupAgro, F-34000 Montpellier - France

^[5] Norwegian Univ Sci & Technol NTNU, Dept ICT & Nat Sci, Alesund - Norway

• Tipo de documento: Artigo Científico
• Fonte: IEEE Geoscience and Remote Sensing Letters; v. 17, n. 8, p. 1450-1454, AUG 2020.
• Citações Web of Science: 1

Resumo

Pixelwise image classification based on time series profiles has been very effective in several applications. In this letter, we investigate recently proposed image-based time series encoding approaches {[}e.g., Gramian angular summation field/Gramian angular difference field (GASF/GADF) and Markov transition field (MTF)] to support the identification of eucalyptus regions in remote sensing images. We perform a comparative study concerning the combination of image-based representations suitable for encoding the most important time series patterns with the ability of state-of-the-art deep-learning-based approaches for characterizing image visual properties. The comparative study demonstrates that the evaluated image representations, combined with different deep learning feature extractors lead to highly effective classification results, which are superior to those of recently proposed methods for time-series-based eucalyptus plantation detection. (AU)

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